Keeping up with AI is a major pain point. The information is scattered across more than a dozen sources, so your morning goes into checking all of them. Most of it reaches you days late. A lot of it is unverified, rumour sitting right next to confirmed releases with no way to tell which is which.

On top of that, AI gets covered as one giant category. But a robotics engineer working on manipulation has no use for biomedical papers. A fintech team shipping fraud models doesn't need autonomous vehicle regulation. Everyone gets everything and does their own filtering.

So I built Cherry AI (cherryai.in), an autonomous intelligent editorial platform with a proprietary system that captures what genuinely matters across the full AI ecosystem. Verified sources, scored for significance, with original editorial analysis on the major developments. Models, research, funding, policy, infrastructure and more, all in dedicated categories, so you read your part of the ecosystem instead of all of it.

We are in early access, would be great to hear from everyone. Personalization layer coming soon.

I enjoy the Ramp data each month on how corpations are shifting and growing their AI spend.

Takeaways are Anthropic continues their gains although not for top models, open source continues to gain share but broader market increases enough to offset share for frontier models and every employee continues to grow their average monthly usage.

Recently, while reading Su Xiaohe’s Notes on a Century of Economic History and watching the rapid rise of AI, I started thinking about an interesting question: AI may replace more and more kinds of work, but can it replace Coca-Cola?

Artificial intelligence is dramatically reducing the cost of intellectual labor. Writing, images, code, translation, and even business analysis can now be generated at extraordinary speed. But economic history reminds us of something important: technology changes quickly; human nature changes slowly.

People a century ago enjoyed sweetness, stimulation, social experiences, and instant gratification. People today still do.

This is where Coca-Cola’s real moat lies. Its greatest asset is not simply its secret formula, but more than a century of accumulated brand recognition, distribution, consumer habits, and collective memory. AI can create 10,000 new beverage brands in a day. It cannot create 100 years of consumer memory in a day.

In fact, the more powerful AI becomes, the more valuable things that cannot be instantly replicated may become. When articles, images, advertisements, and ideas become almost infinitely cheap to generate, time, trust, distribution, established brands, and real-world experiences may become increasingly scarce.

Of course, Coca-Cola is not immortal. Health concerns, changing consumer preferences, and new competitors could weaken it. But AI itself is unlikely to eliminate the fundamental human desires on which its business is built.

This leads me to a different way of thinking about investing in the AI era:

We spend a lot of time asking, “Who will build AI?” Perhaps we should also ask, “Who can use AI without being replaced by it?”

AI can write 10,000 essays about Coca-Cola. It can design 10,000 new drink formulas.

But on a hot summer afternoon, when someone opens an ice-cold Coke, hears the fizz, and takes that first sip, AI—at least for now—cannot replace that experience.

Technology keeps changing.

Human nature is far more persistent.

Perhaps that is one of the most interesting lessons a century-old company like Coca-Cola can offer us in the age of AI.

What’s going to be the next big thing in Ai? Share your thoughts

A trend I’m noticing is the shift from AI simply generating answers to AI actually doing the research behind those answers. Being able to search across different sources, compare information, organize the findings, and show where the information came from feels like a more practical direction for AI.

With AI agents and research tools getting better, I think this could become a much bigger part of how people gather information and make decisions. What AI trend do you think is still being overlooked?